Field of the Invention
The invention concerns a method for generating a magnetic resonance image data set of a target region using a magnetic resonance scanner of the type wherein magnetic resonance data for a number of slices are recorded simultaneously with the recording of magnetic resonance data for the same or fewer number of different slices, wherein magnetic resonance data are assigned to respective individual slices in the multi-slice data set by separation algorithm of the type used in parallel imaging. This separation algorithm uses input parameters determined from a calibration data set of the target region, which was recorded in a reference scan. The magnetic resonance image data set is thereafter reconstructed from the magnetic resonance data assigned to individual slices. The invention further concerns a magnetic resonance apparatus, and a non-transitory electronically readable data storage medium for implementing such a method.
Description of the Prior Art
A problem associated with magnetic resonance (MR) imaging, particularly in the field of medicine when therefore target regions of a patient must be examined, is the duration required for the acquisition of the magnetic resonance data from the subject. In order to accelerate this, a technique known as parallel imaging has been developed, in which the MR data are acquired with multiple receiving coils in multiple receiving channels. As part of the process of parallel imaging, it is possible e.g. to excite a number of slices in a stack of slices and to record multi-slice data from all of these slices simultaneously, such that a multi-slice data set in k-space is composed of the simultaneously recorded slices superimposed one upon the other. In order to separate these slices, and therefore to extract magnetic resonance data relating to individually recorded slices from the multi-slice data set, separation algorithms can be used that were developed for parallel imaging, e.g. the so-called slice-GRAPPA algorithm. This approach can also be combined with other parallel imaging techniques, e.g. the standard GRAPPA or SENSE. Further details about simultaneous multi-slice imaging (SMS imaging) can be found in the article by Setsompop et. al. “Blipped-Controlled Aliasing in Parallel Imaging for Simultaneous Multislice Echo Planar Imaging with Reduced g-Factor Penalty”, Magnetic Resonance in Medicine 67:1210-1224 (2012). The application of SMS imaging to diffusion recordings is described in the article by Setsompop et. al., “Improving diffusion MRI using simultaneous multi-slice echo planar imaging”, NeuroImage 63:569-580 (2012). Both articles also explain the slice-GRAPPA algorithm in detail.
When using the slice-GRAPPA algorithm as a separation algorithm, a GRAPPA-type deconvolution kernel, which is derived from a calibration data set obtained in a reference scan, is used for the slices, such that when applied to the superimposed multi-slice data, k-space points that belong to each individual imaging slice can be determined. If a threefold acceleration is required with respect to the slices, and therefore a second number of three slices in each case are recorded simultaneously in a multi-slice scan, three separate sets of GRAPPA kernels are determined and applied. It is again noted in this case that SMS imaging, i.e. acceleration by recording multiple slices in parallel, can obviously also be enhanced by further acceleration measures, in particular by in-plane acceleration measures, and therefore conventional GRAPPA can also be applied after the slice-GRAPPA algorithm, in order to determine missing k-space lines for those slices with in-plane undersampling.
In the case of the cited diffusion imaging and in a multiplicity of further imaging situations, magnetic resonance data are recorded more than once, and then averaged afterwards. In the example of diffusion imaging, e.g. various b-value scans are performed, wherein magnetic resonance data is usually recorded multiple times in order to reduce the image noise and to increase the reliability of the ADC map calculation. In this context, it may occur in isolated cases that b=0 magnetic resonance image data sets, whose magnetic resonance data were recorded using SMS imaging with coils having a small number of channels, exhibit slice crosstalk artifacts, which can appear, for example, as fat halo signs due to insufficient slice separation.